Florida Tech Shines at Machine Learning for Science Hackathon Competition

florida tech won 1st in Particle Images Challenge

Florida Tech student teams won multiple awards, including one for first place, in the Machine Learning for Science Hackathon Competition. (Florida Tech image)

BREVARD COUNTY • MELBOURNE, FLORIDA – Florida Tech student teams won multiple awards, including one for first place, in the Machine Learning for Science Hackathon Competition.

The two-week event in November featured teams from dozens of schools in the U.S. and around the world.

It focused on applying machine learning techniques to scientific challenges, including those from the fields of physics, astronomy, and planetary science.

There were six main challenges:

■ Higgs Boson Challenge (Classification, General)
■ Particle Images Challenge (Classification, Computer Vision)
■ Strong Lensing Challenge (Multi-class Classification, Regression, Computer Vision)
■ NMR Spin Challenge (Multi-Target Regression)
■ Planetary Albedo (Regression, Image Analysis)
■ Circumgalactic Medium (Dimensionality Reduction, Spectra)

■ In the Particle Images Challenge – Classification challenge, the Florida Tech team of Stephen Butalla and Mehdi Rahmani took first place. The other participating Florida Tech team, comprised of Colbi Appleby, Nico Braukman, and Pragya Jha, was 7th.

■ In the Particle Images Challenge, students had a detector as a camera to identify electrons from photons using any algorithm.

The Appleby-Braukman-Jha team won third place in the NMR Spin Challenge, which used machine learning and condensed matter physics with the goal of predicting the strength and shape of interactions between nuclear spins from simulated time-dependent magnetic curves.

■ In the Strong Lensing Challenge – Classification, the team won 5th place. That challenge saw students design and implement unsupervised deep learning models to solve the task of anomaly detection on strong lensing images

Machine Learning for Science is an open-source organization that brings together modern machine learning techniques and applies them to cutting-edge problems in science, technology, engineering, and math (STEM).

For questions or additional information, please contact Marc Baarmand at baarmand@fit.edu.

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